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Data Exchange between Vehicle and Power System for Optimal Charging
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Data Exchange between Vehicle and Power System for Optimal Charging

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 8985

Special Issue Editors

Dr. Jennifer Leijon

E-Mail Website
Guest Editor
Division of Electricity, Department of Electrical Engineering, Uppsala University, 75236 Uppsala, Sweden
Interests: electric vehicles; charging; renewable energy systems
Dr. Boel Ekergård

E-Mail Website
Guest Editor
Division of Industrial Engineering and Management, Electrical- and Mechanical Engineering, University West, Trollhättan, Sweden
Interests: electric vehicles; electric motors; renewable energy systems
Dr. Valeria Castellucci

E-Mail Website
Guest Editor
Division of Electricity, Department of Electrical Engineering, Uppsala University, 75236 Uppsala, Sweden
Interests: electric vehicles; charging; renewable energy systems

Special Issue Information

Dear Colleagues, 

The data sharing between charging/discharging electric vehicles and power systems is important for a successful interaction of the vehicles with the grid. Information exchange is also needed at the design stage, as well as in the control of the electric vehicle motor and battery system. Moreover, data resolution, security, and sustainability aspects need to be taken into account. In the near future, we will likely see different types of electrified vehicles on the market (trucks, cars, boats, aircraft, autonomous vehicles, etc.), new designs in propulsion systems, and smart charging strategies (cable charging, wireless charging, battery swapping, V2G, high-power charging, etc.) for different applications and in different environments (buildings, airports, construction sites, etc.).  

In this context, we welcome you to submit manuscripts in areas such as, but not limited to:

  • Data exchange between electric vehicles and power systems.
  • Data sharing when implementing different charging strategies.
  • Interaction between the motor and the battery system with respect to, e.g., regenerative charging.
  • Vehicle-to-grid (V2G), vehicle-to-everything (V2X), etc., including pros and cons. Smart charging strategies, algorithms, and charging standards.
  • Energy system modelling, including, e.g., renewable energy systems, FEM or real-time simulations, and experimental work.
  • Energy management systems.
  • Security and safety aspects.
  • Equipment and measurement systems for vehicle-grid interaction.

Dr. Jennifer Leijon
Dr. Boel Ekergård
Dr. Valeria Castellucci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electric vehicle charging
  • data exchange
  • power system
  • electric motor
  • security
  • vehicle-to-everything
  • vehicle-to-grid
  • electric vehicle battery system
  • renewable energy systems

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Published Papers (5 papers)

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Research

23 pages, 6635 KiB  
Article
Data-Driven Modeling of Electric Vehicle Charging Sessions Based on Machine Learning Techniques
by Raymond O. Kene and Thomas O. Olwal
World Electr. Veh. J. 2025, 16(2), 107; https://doi.org/10.3390/wevj16020107 - 16 Feb 2025
Viewed by 449
Abstract
The increased demand for electricity is inevitable due to transport sector electrification. A major part of this demand is from electric vehicle (EV) charging on a large scale, which is now a growing concern for the grid power distribution system. The lack of [...] Read more.
The increased demand for electricity is inevitable due to transport sector electrification. A major part of this demand is from electric vehicle (EV) charging on a large scale, which is now a growing concern for the grid power distribution system. The lack of insight into grid energy demand by EVs makes it difficult to manage these consumptions on a large scale. For any grid load management application to be effective in minimizing the impact of uncontrolled charging, there is a need to gain insight into EV energy demand. To address this issue, this study presents data-driven modeling of EV charging sessions based on machine learning (ML) techniques. The purpose of using ML as an approach is to provide insight for estimating future energy demand and minimizing the impact of EV charging on the grid. To achieve the aim of this study, firstly, we investigated the impact of large-scale charging of EVs on the grid. Based on this, we formulated an objective function, expressed as a sum of utility functions when EVs charge on the grid with constraints imposed on voltage levels and charging power. Secondly, we employed a graphical modeling approach to study the temporal distribution of EV energy consumption based on real-world datasets from EV charging sessions. Thirdly, using ML regression models, we predicted EV energy consumption using four different models of fine tree, linear regression, linear SVM (support vector machine), and neural network. We used 5-fold cross-validation to protect against overfitting and evaluated the performances of these models using regression analysis metrics. The results from our predictions showed better accuracy when compared with the results from the work of other authors. Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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20 pages, 5342 KiB  
Article
Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks
by Christina V. Grammenou, Magdalini Dragatsika and Aggelos S. Bouhouras
World Electr. Veh. J. 2024, 15(10), 462; https://doi.org/10.3390/wevj15100462 - 11 Oct 2024
Viewed by 1201
Abstract
In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in [...] Read more.
In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction. Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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33 pages, 15119 KiB  
Article
Optimized Integration of Medium-Voltage Multimegawatt DC Charging Stations: Concepts, Guidelines and Analysis
by Sumanta Biswas, Cham Kpu Gerald, Barbara Herndler, Daniel Stahleder, Yannick Wimmer and Markus Makoschitz
World Electr. Veh. J. 2024, 15(10), 450; https://doi.org/10.3390/wevj15100450 - 3 Oct 2024
Viewed by 4267
Abstract
The integration of multimegawatt fast chargers into local distribution grids is becoming increasingly relevant due to recent initiatives to push for higher charging power, especially for applications like heavy-duty vehicles. However, the high-power capacity of these chargers, especially when multiple units operate simultaneously [...] Read more.
The integration of multimegawatt fast chargers into local distribution grids is becoming increasingly relevant due to recent initiatives to push for higher charging power, especially for applications like heavy-duty vehicles. However, the high-power capacity of these chargers, especially when multiple units operate simultaneously at specific locations, raises several important considerations for the optimal design and integration of multimegawatt fast chargers. These include, for example, power electronics architectures and dedicated designs, grid stability, and the incorporation of renewable energy systems. Thus, this paper provides a comprehensive analysis of the key factors influencing the optimal integration of these ultra-high-power chargers, looking into impacts on medium-voltage (MV) networks, the design considerations for medium-voltage power electronics in DC chargers, and the potential of renewable energy systems to offset grid demand. Additionally, this paper explores the potential high-level communication requirements necessary for efficient and reliable charger operation, including a proposal for a robust communication interface layer stack. This investigation aims to provide a holistic understanding of the challenges and opportunities associated with integrating multimegawatt fast chargers into existing power systems, offering insights into the enhancement of both performance and sustainability. Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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17 pages, 3082 KiB  
Article
Study of an Electric Vehicle Charging Strategy Considering Split-Phase Voltage Quality
by Fulu Yan, Mian Hua, Feng Zhao and Xuan Liang
World Electr. Veh. J. 2024, 15(7), 315; https://doi.org/10.3390/wevj15070315 - 18 Jul 2024
Viewed by 988
Abstract
Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is [...] Read more.
Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is proposed in this paper. Issues with voltage unbalance (VU), split-phase voltage deviation (VD), and split-phase voltage harmonics (VHs) are included in the optimization objective model. An upgraded version of the multi-objective non-dominated sorting genetic algorithm (NSGA-II) is used in the inner layer of the model and to pass the generated EV phase selection scheme to the outer layer. The outer layer consists of a split-phase harmonic current algorithm based on the forward–backward generation method, and feeds the voltage quality calculation results to the inner layer. After several iterations, the optimal EV phase selection scheme can be obtained when the inner layer algorithm satisfies the convergence condition. The results gained for the example indicate that the suggested EV charging approach can effectively handle the PDN’s split-phase voltage quality. Furthermore, it enhances the energy efficiency of PDN operations and promotes further energy consumption. Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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27 pages, 4090 KiB  
Article
An Effective Strategy for Achieving Economic Reliability by Optimal Coordination of Hybrid Thermal–Wind–EV System in a Deregulated System
by Ravindranadh Chowdary Vankina, Sadhan Gope, Subhojit Dawn, Ahmed Al Mansur and Taha Selim Ustun
World Electr. Veh. J. 2024, 15(7), 289; https://doi.org/10.3390/wevj15070289 - 28 Jun 2024
Cited by 2 | Viewed by 924
Abstract
This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level [...] Read more.
This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level of the EV battery storage system. In a renewable-associated power network, renewable energy producers must submit power supply proposals to the system operator at least one day before operations begin. The market managers then combine the power plans for the next several days based on bids from both power providers and distributors. However, due to the unpredictable nature of renewable resources, the electrical system cannot exactly adhere to the predefined power supply criteria. When true and estimated renewable power generation diverges, the electrical system may experience an excess or shortage of electricity. If there is a disparity between true and estimated wind power (TWP, EWP), the EV plant operates to minimize this variation. This lowers the costs associated with the discrepancy between actual and projected wind speeds (TWS, EWS). The proposed method effectively reduces the uncertainty associated with wind generation while being economically feasible, which is especially important in a deregulated power market. This study proposes four separate energy levels for an EV battery storage system (EEV,max, EEV,opt, EEV,low, and EEV,min) to increase system profit and revenue, which is unique to this work. The optimum operating of these EV battery energy levels is determined by the present electric grid frequency and the condition of TWP and EWP. The proposed approach is tested on a modified IEEE 30 bus system and compared to an existing strategy to demonstrate its effectiveness and superiority. The entire work was completed using the optimization technique called sequential quadratic programming (SQP). Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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